多维度结构化稀疏MIMO雷达稳健角度估计方法研究

多输入多输出(MIMO: Multiple-Input Multiple-Output)雷达同时利用信号的空域分集增益和波形分集增益，比传统相控阵雷达具有更多的优势。为了实现对弱小目标进行有效地探测与阻击，需要对其进行准确地辨别定位。因此目标角度估计是MIMO雷达的核心内容之一。在实际应用系统中，MIMO 雷达面临低信噪比、小样本和阵列误差的情况，导致传统角度估计算法性能急骤下降甚至失效。故本项目通过利用信号的多维度结构化稀疏特性，研究低信噪比、小样本和阵列误差条件下MIMO雷达稳健角度估计的新理论和新方法。针对信号的多维度结构特性，提出基于张量分解的稳健角度估计方法；针对信号的空域稀疏结构特性，提出基于稀疏信号重构的稳健角度估计方法，最后联合信号的多维度与稀疏结构特性，提出基于多维度结构化稀疏重构的稳健角度估计方法，解决低信噪比、小样本和阵列误差条件下，MIMO雷达的稳健高精度角度问题。

Multiple-input multiple-output (MIMO) radar simultaneously uses the spatial diversity gain and waveform diversity gain， and it provides more advantages than conventional phased-array radar. In order to detect and intercept the weak small targets effectively, these targets need to identify and locate correctly. Thus, angle estimation is a key issue in MIMO radar. In the practice MIMO radar system, the conventional angle estimation algorithms fail to work or their performance degrade remarkably in the present of lower SNR, smaller number of snapshot, and array errors. Therefore, this project focus on robust angle estimation algorithms for MIMO radar by utilizing the multi-dimensional structured sparsity of the signals in the presence of lower SNR, smaller number of snapshot, and array errors. For the multi-dimensional structured characteristic of the signals, tensor decomposition based robust angle estimation algorithms are proposed. Then for the spatial sparse structured characteristic of the signals, sparse signal recovery based robust angle estimation algorithms are proposed. Finally，combining both the multi-dimensional and spatial sparse structured characteristic，multi-dimensional structured sparse recovery based robust angle estimation algorithms are proposed，which can achieve robust high resolution angle estimation in the presence of lower SNR, smaller number of snapshot, and array errors.